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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Iterator, List, Optional, Tuple

import torch
from torch import nn
from torch.distributions import Categorical

from kornia.augmentation.auto.base import SUBPOLICY_CONFIG, PolicyAugmentBase
from kornia.augmentation.auto.operations.policy import PolicySequential
from kornia.augmentation.auto.rand_augment import ops
from kornia.augmentation.container.params import ParamItem

default_policy: List[SUBPOLICY_CONFIG] = [
    # [("identity", 0, 1)],
    [("auto_contrast", 0, 1)],
    [("equalize", 0, 1)],
    [("rotate", -30.0, 30.0)],
    [("posterize", 0.0, 4)],
    [("solarize", 0.0, 1.0)],
    # (Color, 0.1, 1.9),
    [("contrast", 0.1, 1.9)],
    [("brightness", 0.1, 1.9)],
    [("sharpness", 0.1, 1.9)],
    [("shear_x", -0.3, 0.3)],
    [("shear_y", -0.3, 0.3)],
    [("translate_x", -0.5, 0.5)],
    [("translate_y", -0.5, 0.5)],
]


class TrivialAugment(PolicyAugmentBase):
    """Apply TrivialAugment :cite:`muller2021trivialaugment` augmentation strategies.

    Args:
        policy: candidate transformations. If None, a default candidate list will be used.
        transformation_matrix_mode: computation mode for the chained transformation matrix, via `.transform_matrix`
                                    attribute.
                                    If `silent`, transformation matrix will be computed silently and the non-rigid
                                    modules will be ignored as identity transformations.
                                    If `rigid`, transformation matrix will be computed silently and the non-rigid
                                    modules will trigger errors.
                                    If `skip`, transformation matrix will be totally ignored.

    Examples:
        >>> import kornia.augmentation as K
        >>> in_tensor = torch.rand(5, 3, 30, 30)
        >>> aug = K.AugmentationSequential(TrivialAugment())
        >>> aug(in_tensor).shape
        torch.Size([5, 3, 30, 30])

    """

    def __init__(
        self, policy: Optional[List[SUBPOLICY_CONFIG]] = None, transformation_matrix_mode: str = "silent"
    ) -> None:
        if policy is None:
            _policy = default_policy
        else:
            _policy = policy

        super().__init__(_policy, transformation_matrix_mode=transformation_matrix_mode)
        selection_weights = torch.tensor([1.0 / len(self)] * len(self))
        self.rand_selector = Categorical(selection_weights)

    def compose_subpolicy_sequential(self, subpolicy: SUBPOLICY_CONFIG) -> PolicySequential:
        if len(subpolicy) != 1:
            raise RuntimeError(f"Each policy must have only one operation for TrivialAugment. Got {len(subpolicy)}.")
        name, low, high = subpolicy[0]
        return PolicySequential(*[getattr(ops, name)(low, high)])

    def get_forward_sequence(self, params: Optional[List[ParamItem]] = None) -> Iterator[Tuple[str, nn.Module]]:
        if params is None:
            idx = self.rand_selector.sample((1,))
            return self.get_children_by_indices(idx)

        return self.get_children_by_params(params)
